| name | data-synthesis |
| description | Qualitative and quantitative data synthesis — affinity mapping, thematic analysis, statistical frameworks, insight triangulation, and research-to-spec pipelines |
Data Synthesis -- Research Intelligence
Skill pack for transforming raw research data into structured insights. Covers affinity mapping, thematic analysis, quantitative aggregation, triangulation, insight scoring, gap analysis, and integration with the Memoire research pipeline.
Core Loop
COLLECT RAW DATA --> ORGANIZE --> CODE --> THEME --> TRIANGULATE --> SCORE --> OUTPUT INSIGHTS
Every synthesis pass must produce actionable insights, not summaries. An insight is a non-obvious finding that changes a design decision.
Affinity Mapping Methodology
What It Is
A bottom-up clustering technique. Individual observations (sticky notes, quotes, data points) are grouped by natural similarity until themes emerge.
Process
| Step | Action | Output |
|---|
| 1. Harvest | Extract individual observations from raw data. One fact per unit. | 50-300 discrete data points |
| 2. Spread | Lay all data points out without grouping. Randomize order. | Ungrouped pool |
| 3. Cluster | Silently group related points. Move items that feel similar together. | 8-20 raw clusters |
| 4. Label | Name each cluster with a descriptive header that captures the shared meaning. | Named clusters |
| 5. Supergroup | Group clusters into 3-6 high-level themes. | Theme hierarchy |
| 6. Prioritize | Rank themes by frequency, severity, and opportunity. | Prioritized theme map |
Rules
- One observation per data point -- never combine two findings into one note
- Group by meaning, not by source (do not cluster "all interview 3 data" together)
- If a cluster has more than 15 items, split it
- If a cluster has fewer than 3 items, consider merging or flagging as an outlier
- Relabel clusters after regrouping -- initial labels often drift from final content
In Memoire
memi research from-stickies
memi research synthesize
memi research report
Thematic Analysis
Three Coding Phases
Phase 1: Open Coding
Read through all data and assign descriptive codes to each meaningful segment.
| Raw Data | Open Code |
|---|
| "I always forget where I saved the file" | file-location-confusion |
| "The search never finds what I need" | search-ineffective |
| "I end up asking a colleague where things are" | workaround-social |
Rules:
- Code everything relevant, skip nothing on first pass
- Use gerunds or descriptive phrases (not single words)
- One segment can have multiple codes
- Target 30-80 unique codes from a typical study
Phase 2: Axial Coding
Group open codes into categories. Identify relationships between categories.
| Category | Open Codes |
|---|
| Navigation Failure | file-location-confusion, search-ineffective, breadcrumb-ignored |
| Social Workarounds | workaround-social, slack-channel-for-files, ask-manager |
| Mental Model Mismatch | expected-folder-structure, alphabetical-assumption, recency-bias |
Relationships to identify:
- Causal: A leads to B ("navigation failure causes social workarounds")
- Contextual: A happens during B ("mental model mismatch occurs during onboarding")
- Strategic: A is a response to B ("social workarounds compensate for navigation failure")
Phase 3: Selective Coding
Identify the core theme that connects all categories. This becomes the central narrative.
Core Theme: "Users build parallel information architectures through social channels
because the system's structure does not match their mental models."
Code Quality Checklist
Quantitative Aggregation
Standard Metrics
| Metric | Formula | Use Case |
|---|
| Mean | Sum / Count | Average satisfaction, task time |
| Median | Middle value (sorted) | Task time (resistant to outliers) |
| Mode | Most frequent value | Preferred option selection |
| Std Dev | sqrt(variance) | Spread of responses |
| NPS | %Promoters - %Detractors | Overall loyalty/satisfaction |
| SUS Score | ((Sum of odd - 5) + (25 - Sum of even)) * 2.5 | System usability |
| Task Success Rate | Successes / Total Attempts * 100 | Effectiveness |
| Time on Task | Median completion time | Efficiency |
| Error Rate | Errors / Opportunities * 100 | Error-proneness |
NPS Calculation
Responses on 0-10 scale:
Detractors: 0-6
Passives: 7-8
Promoters: 9-10
NPS = (Promoter Count / Total) * 100 - (Detractor Count / Total) * 100
Range: -100 to +100
| NPS Range | Interpretation |
|---|
| -100 to 0 | Critical -- systemic issues |
| 0 to 30 | Below average -- improvement needed |
| 30 to 50 | Good -- competitive |
| 50 to 70 | Excellent -- strong loyalty |
| 70 to 100 | World-class |
SUS Score Interpretation
| SUS Score | Grade | Percentile |
|---|
| < 51 | F | Bottom 15% |
| 51-68 | D | 15-50% |
| 68-74 | C | 50-70% |
| 74-80 | B | 70-85% |
| 80-90 | A | 85-97% |
| > 90 | A+ | Top 3% |
Statistical Significance
- For A/B comparisons: use chi-squared test (categorical) or t-test (continuous)
- Minimum sample size for usability: 5 per segment (qualitative), 30+ per variant (quantitative)
- Report confidence intervals, not just point estimates
- Flag any metric based on fewer than 5 data points as unreliable
Triangulation Across Sources
Triangulation Matrix
Cross-reference findings across at least 3 data sources to validate strength.
| Finding | Interviews | Surveys | Analytics | Usability Tests | Strength |
|---|
| Users abandon search after 2 attempts | Mentioned by 4/6 | 62% agree | Avg 1.8 queries before exit | 3/5 gave up | STRONG |
| Mobile nav is confusing | 2/6 mentioned | 28% agree | Mobile bounce 2x desktop | Not tested | MODERATE |
| Dark mode is desired | 1/6 mentioned | 71% want it | N/A | N/A | WEAK (conflicting) |
Strength Classification
| Level | Criteria |
|---|
| STRONG | Confirmed by 3+ independent sources with consistent direction |
| MODERATE | Confirmed by 2 sources, or 3 sources with some inconsistency |
| WEAK | Single source, or multiple sources with conflicting signals |
| CONTRADICTED | Sources actively disagree -- requires deeper investigation |
Insight Quality Scoring
Rate every insight on four dimensions before including it in a deliverable.
| Dimension | Score 1 | Score 3 | Score 5 |
|---|
| Evidence | Single anecdote | Multiple data points, one source | Multiple data points, multiple sources |
| Actionability | Vague observation | Suggests a direction | Points to a specific design change |
| Novelty | Already known by team | Partially known | Genuinely new understanding |
| Impact | Affects edge cases | Affects a segment | Affects most users |
Threshold: Only insights scoring 12+ (out of 20) go into the final report. Insights scoring 8-11 go into an appendix. Below 8, discard or flag for follow-up research.
Insight Statement Format
[WHO] experiences [WHAT PROBLEM/BEHAVIOR]
because [WHY/ROOT CAUSE],
which means [IMPLICATION FOR DESIGN].
Example:
New users abandon the onboarding wizard at step 3
because the required fields feel invasive for a first interaction,
which means we should defer profile completion to post-activation.
Gap Analysis Frameworks
Coverage Gap Matrix
Map research questions against data sources to identify blind spots.
| Research Question | Source A | Source B | Source C | Gap? |
|---|
| Why do users churn at day 7? | Partial | No data | Partial | YES |
| What drives upgrade decisions? | Full | Full | Partial | No |
| How do teams collaborate? | No data | No data | Full | YES |
Types of Gaps
| Gap Type | Description | Resolution |
|---|
| Data gap | No data collected on this question | Plan follow-up study |
| Segment gap | Data exists but not for a key user segment | Recruit missing segment |
| Depth gap | Quantitative data but no qualitative understanding | Conduct interviews |
| Recency gap | Data is older than 6 months | Refresh study |
| Context gap | Lab data but no field observation | Run diary study or contextual inquiry |
Integration with Memoire Research Pipeline
Pipeline Stages
RAW DATA (Excel, CSV, stickies)
|
v
memi research from-file <path> # Ingest raw data
|
v
memi research from-stickies # Pull Figma sticky notes
|
v
memi research synthesize # Affinity map + thematic analysis
| # Outputs coded themes + scored insights
v
memi research report # Structured insight report
| # Markdown + Figma sticky output
v
memi spec component <name> # Insights feed component specs
memi spec page <name> # Insights feed page specs
memi compose "dashboard from research" # Agent builds dashboard from insights
Data Formats
| Input Format | Command | Notes |
|---|
| Excel (.xlsx) | from-file | One sheet per study; row 1 = headers |
| CSV (.csv) | from-file | UTF-8 encoding required |
| Figma stickies | from-stickies | One observation per sticky |
| JSON | from-file | Array of { text, source, participant?, timestamp? } |
Output Artifacts
| Artifact | Format | Destination |
|---|
| Coded themes | JSON | .memoire/research/themes.json |
| Insight cards | JSON + Markdown | .memoire/research/insights/ |
| Affinity map | Figma stickies | Canvas (grouped by theme) |
| Quantitative summary | JSON | .memoire/research/metrics.json |
| Final report | Markdown | .memoire/research/report.md |
Connecting Insights to Specs
When generating component or page specs, reference insight IDs:
{
"name": "OnboardingWizard",
"level": "organism",
"researchInsights": ["INS-007", "INS-012"],
"designRationale": "Deferred profile completion based on day-1 drop-off finding (INS-007)"
}
This creates traceability from research through spec to generated code.
